#28 North Carolina State (13-8)

avg: 1773.66  •  sd: 50.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
65 Massachusetts Win 9-6 1814.85 Feb 9th Queen City Tune Up 2019 Women
40 Michigan Win 12-4 2169.43 Feb 9th Queen City Tune Up 2019 Women
26 Georgia Loss 8-10 1585.63 Feb 9th Queen City Tune Up 2019 Women
5 Carleton College-Syzygy Loss 6-9 1846.93 Feb 9th Queen City Tune Up 2019 Women
69 Notre Dame Win 15-4 1928.85 Feb 10th Queen City Tune Up 2019 Women
65 Massachusetts Win 11-6 1942.98 Feb 10th Queen City Tune Up 2019 Women
41 Harvard Loss 10-13 1239.51 Feb 10th Queen City Tune Up 2019 Women
1 North Carolina Loss 7-13 1972.54 Feb 23rd Commonwealth Cup 2019
22 Tufts Win 11-10 2059.61 Feb 23rd Commonwealth Cup 2019
58 Penn State Win 13-7 2008.57 Feb 23rd Commonwealth Cup 2019
45 Virginia Win 12-11 1670.7 Feb 24th Commonwealth Cup 2019
11 Pittsburgh Loss 9-12 1737.9 Feb 24th Commonwealth Cup 2019
59 Duke Win 13-9 1864.53 Mar 7th Atlantic Coast Showcase 3719
1 North Carolina Loss 7-13 1972.54 Mar 21st Atlantic Coast Showcase 32119
20 North Carolina-Wilmington Loss 6-13 1360.18 Mar 29th Atlantic Coast Showcase 32919
85 Dayton Win 13-4 1843.31 Mar 30th I 85 Rodeo 2019
105 Liberty Win 11-6 1633.88 Mar 30th I 85 Rodeo 2019
36 Vanderbilt Win 12-9 2018.65 Mar 30th I 85 Rodeo 2019
57 Cornell Win 14-12 1681.58 Mar 31st I 85 Rodeo 2019
82 Georgetown Win 14-11 1579.8 Mar 31st I 85 Rodeo 2019
18 South Carolina Loss 9-13 1552.85 Mar 31st I 85 Rodeo 2019
**Blowout Eligible

FAQ

The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)